Jun 14, 2021. By S V Aditya
Multicloud deployments are now practically ubiquitous. Among cloud adopters, 92% of enterprises have a multi-cloud strategy(2021 Flexera State of the Cloud Report). A multi cloud strategy prevents the enterprise from being locked in with a vendor, enables price negotiations, and makes the IT infrastructure more resistant to outages. While these are the obvious advantages, enterprises are still not getting the maximum value from their multi cloud strategy.
Close to half of enterprises are still using their multi cloud architectures to deploy apps in silos across different clouds. This results in a severely unoptimized deployment that consumes more resources than necessary while still having issues of latency and performance. One of the key reasons preventing the migration to more complex architectures is that they are more difficult to manage. Modern architectures often split applications into services run on different platforms. Not managing these services effectively could result in higher costs than a single cloud operation. Moreover, there are other risks like security (e.g. containerization), optimal resource allocation (e.g. edge cloud computing) as well as issues with configurations. Enterprises on average use two to three public and private clouds. Each of these services has unique tools with different controls and configurations. This results in a proliferation of tools that makes it harder for ITOps teams to manage. As a consequence, human errors in configuration are the biggest consequence of outages (Cloud Security Alliance) Compliance and governance also gain complexity when spanning across so many environments as enforcement becomes more difficult. Different security controls between cloud services mean that ITSecOps teams have to work harder to incorporate the enterprise security framework into individual workloads split across different services. Moreover, the adoption of multi-cloud governance and security tools is still low - leaving enterprises with weaker controls mechanisms. Close to four in five cloud users encountered significant security concerns in addition to risks of data loss and leakage (Cloud Security Alliance survey).2
At the same time enterprises' needs for IT resources are growing every year. 31% of enterprises are spending more than $ 12 million annually on public clouds in 2020, up from just 16% in 2019(2021 Flexera State of the Cloud Report). When organizations deploy into multiple clouds they also need more people in their ITOps teams with the experience in these platforms, also increasing the cost in FTEs of managing multiple environments. Moreover, enterprises also have to keep up with the pace of rapid change of technology - which primarily falls on ITOps teams. Consequently, service updates resulting in device configuration changes is the second most common reason for outages. Whenever an enterprise migrates a legacy app to a modern microservices architecture or deploys it into containers, it is the ITOps team that has to manage it, ensure its availability while keeping the cost associated with managing it as low as possible. ITOps teams also have to rapidly learn new architectures and get themselves accustomed to migrating and managing legacy items. These are high value development activities that benefit the enterprise in the long run. However, the time taken up by operational activities prevents them from having enough resources to for development activities. As a consequence, enterprises are left with visibility, security, compliance, configuration, and orchestration challenges along with increasing costs. So how can enterprises manage both their growing requirements, escalating costs while continuing to innovate?
Fortunately, AIOps has the answer. AIOps applies artificial intelligence technology to IT Operations to create a highly scalable ITOps management solution. AIOps solves the challenges of visibility by creating a single pane of glass solution that integrates with multiple cloud services to collect all relevant information. It then uses AI-enabled technologies to improve upon the core reason for monitoring - incident management. Incident Recognition built with AI can analyze the root cause of outages and faults by correlating performance metrics with logs and traces. Anomaly Detection, on the other hand, finds irregular behaviour in device or software performance by analysing KPIs and logs. Both of these together create a safety net for ITOps teams that cover both the Knowns and the Unknowns of IT operational issues. Consequently, teams are faster at fixing issues because the mean time to diagnose them becomes much smaller. AIOps enables low touch configuration and orchestration using an automation engine that is powered with AI models to make its decisions. These engines control agents that can integrate with multiple cloud service provider platforms and configure them based on user needs. Moreover, once it has captured the steady state of the system, it can explore options for optimizing the system by tweaking controls that are permitted to it. On the other hand, it can also lockdown access to these same controls to enforce governance and compliance. This leads to a secure low-touch governance solution that does not rely on manual processes like it does in most ITOps teams today.
AIOps holds vast potential in shaping the enterprise adoption of multi-cloud strategy by making it cheaper, more secure, more transparent and automated in its functionality. To learn more about AIOps, visit Algomox AIOps Page.